Coronaviruses (CoV) are a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV).
Coronavirus disease (COVID-19) is a new strain that was discovered in 2019 and has not been previously identified in humans.
Common signs of infection include respiratory symptoms, fever, cough, shortness of breath and breathing difficulties. In more severe cases, infection can cause pneumonia, severe acute respiratory syndrome, kidney failure and even death.
Standard recommendations to prevent infection spread include regular hand washing, covering mouth and nose when coughing and sneezing, thoroughly cooking meat and eggs. Avoid close contact with anyone showing symptoms of respiratory illness such as coughing and sneezing..
LINKS: DONATE, KNOW MORE, JHU, CONTRIBUTORS
Map
---
title: "Covid-19 Tracker"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: fill
---
```{r setup, include=FALSE}
###-----Packages-----
library(flexdashboard)
library(dplyr)
library(plotly)
library(devtools)
library(knitr)
library(RCurl)
library(tidyr)
library(tidyverse)
###----Input Date to Dataframe----
coronavirus <- read.csv("https://raw.githubusercontent.com/RamiKrispin/coronavirus-csv/master/coronavirus_dataset.csv", header = TRUE)
###-----Parameters-----
confirmed_color <- "royalblue"
active_color <- "darkslateblue"
recovered_color <- "green"
death_color <- "red"
###--- Data Preparation------
df <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(names_from = type,
values_from = total) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = Country.Region) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(names_from = type,
values_from = total) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
recovered_cum = cumsum(recovered),
active_cum = cumsum(active))
df_Country <- coronavirus %>%
dplyr::group_by(date, type, Country.Region) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(names_from = type,
values_from = total) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
recovered_cum = cumsum(recovered),
active_cum = cumsum(active))
df1 <- coronavirus %>%
dplyr:: mutate(date = as.Date(date))%>%
dplyr::filter(date == max(date))
```
SUMMARY DASHBOARD
===============================================================================
row {row-height=100}
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total Confirmed Cases",
icon = "fas fa-user-md",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1),
"%)", sep = ""),
caption = "Active Cases", icon = "fas fa-hospital",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df$recovered, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$recovered, na.rm = TRUE) / sum(df$confirmed), 1),
"%)", sep = ""),
caption = "Recovered Cases", icon = "fas fa-running",
color = recovered_color)
```
### deaths {.value-box}
```{r}
valueBox(value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)", sep = ""),
caption = "Death Cases",
icon = "fas fa-heart-broken",
color = death_color)
```
Column {data-width=650}
-----------------------------------------------------------------------
### CASES BY COUNTRY (TOP 25)
```{r}
plotly::plot_ly(data = df[1:25,],
x = ~ country,
y = ~ unrecovered,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Active",
marker = list(color = active_color)) %>%
plotly::add_trace(y = ~ recovered,
# text = ~ recovered,
# textposition = 'auto',
name = "Recovered",
marker = list(color = recovered_color)) %>%
plotly::add_trace(y = ~ death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = death_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases (log scaled)",
type = "log"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```
Column {data-width=350}
-----------------------------------------------------------------------
### DAILY CUMULATIVE BY TYPE
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cum,
type = "scatter",
mode = "lines+markers",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cum,
type = "scatter",
mode = "lines+markers",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cum,
type = "scatter",
mode = 'lines+markers',
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)) %>%
plotly::layout(title = "",
yaxis = list(title = "Cum. #Cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare")
```
### CARE
```{r}
```
__Coronaviruses (CoV)__ are a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV).
Coronavirus disease (COVID-19) is a new strain that was discovered in 2019 and has not been previously identified in humans.
Common signs of infection include respiratory symptoms, fever, cough, shortness of breath and breathing difficulties. In more severe cases, infection can cause pneumonia, severe acute respiratory syndrome, kidney failure and even death.
Standard recommendations to prevent infection spread include regular hand washing, covering mouth and nose when coughing and sneezing, thoroughly cooking meat and eggs. Avoid close contact with anyone showing symptoms of respiratory illness such as coughing and sneezing..
LINKS:
[DONATE](https://www.who.int/emergencies/diseases/novel-coronavirus-2019/donate),
[KNOW MORE](https://www.who.int/emergencies/diseases/novel-coronavirus-2019),
[JHU](https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6),
[CONTRIBUTORS](https://github.com/RamiKrispin)
HOT SPOTS -1
=====================================================================================
row {.tabset}
-------------------------------------
### AMERICAS
```{r}
daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::mutate(country = Country.Region) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ date,
y = ~ US,
type = "scatter",
mode = "lines+markers",
name = "US") %>%
plotly::add_trace(x = ~ date,
y = ~ Canada,
type = "scatter",
mode = "lines+markers",
name = "Canada") %>%
plotly::add_trace(x = ~ date,
y = ~ Brazil,
type = "scatter",
mode = "lines+markers",
name = "Brazil") %>%
plotly::add_trace(x = ~ date,
y = ~ Mexico,
type = "scatter",
mode = "lines+markers",
name = "Mexico") %>%
plotly::layout(title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Daily Cases"),
xaxis = list(title = ""),
# paper_bgcolor = "black",
plot_bgcolor = "#ffcccc",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```
### GC & OCEANIA
```{r}
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ date,
y = ~ China,
type = "scatter",
mode = "lines+markers",
name = "China") %>%
plotly::add_trace(x = ~ date,
y = ~ `Korea, South`,
type = "scatter",
mode = "lines+markers",
name = "South Korea") %>%
plotly::add_trace(x = ~ date,
y = ~ Japan,
type = "scatter",
mode = "lines+markers",
name = "Japan") %>%
plotly::add_trace(x = ~ date,
y = ~ `New Zealand`,
type = "scatter",
mode = "lines+markers",
name = "New Zealand") %>%
plotly::add_trace(x = ~ date,
y = ~ Australia,
type = "scatter",
mode = "lines+markers",
name = "Australia") %>%
plotly::layout(title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Daily Cases"),
xaxis = list(title = ""),
# paper_bgcolor = "black",
plot_bgcolor = "#ccffcc",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```
row {.tabset}
-------------------------------------
### SAP
```{r}
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ date,
y = ~ India,
type = "scatter",
mode = "lines+markers",
name = "India") %>%
plotly::add_trace(x = ~ date,
y = ~ Pakistan,
type = "scatter",
mode = "lines+markers",
name = "Pakistan") %>%
plotly::add_trace(x = ~ date,
y = ~ Bangladesh,
type = "scatter",
mode = "lines+markers",
name = "Bangladesh") %>%
plotly::add_trace(x = ~ date,
y = ~ Malaysia,
type = "scatter",
mode = "lines+markers",
name = "Malaysia") %>%
plotly::add_trace(x = ~ date,
y = ~ Indonesia,
type = "scatter",
mode = "lines+markers",
name = "Indonesia") %>%
plotly::layout(title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Daily Cases"),
xaxis = list(title = ""),
# paper_bgcolor = "black",
plot_bgcolor = "#ccccff",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```
### EUROPE
```{r}
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ date,
y = ~ Italy,
type = "scatter",
mode = "lines+markers",
name = "Italy") %>%
plotly::add_trace(x = ~ date,
y = ~ Spain,
type = "scatter",
mode = "lines+markers",
name = "Spain") %>%
plotly::add_trace(x = ~ date,
y = ~ Germany,
type = "scatter",
mode = "lines+markers",
name = "Germany") %>%
plotly::add_trace(x = ~ date,
y = ~ `United Kingdom`,
type = "scatter",
mode = "lines+markers",
name = "UK") %>%
plotly::add_trace(x = ~ date,
y = ~ France,
type = "scatter",
mode = "lines+markers",
name = "France") %>%
plotly::add_trace(x = ~ date,
y = ~ France,
type = "scatter",
mode = "lines+markers",
name = "Spain") %>%
plotly::layout(title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Daily Cases"),
xaxis = list(title = ""),
# paper_bgcolor = "black",
plot_bgcolor = "#ccffff",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```
HOT SPOTS -2
===================================================================================
row
-----------------------------------------------------------------------
### Cum Confirmed Cases by Country
```{r}
df_Country_Confirmed <- df_Country %>%
dplyr:: select(date, Country.Region, confirmed)%>%
tidyr::pivot_wider(names_from = Country.Region,
values_from = confirmed) %>%
dplyr::select(date, US, China, Italy, Iran, Germany, `Korea, South`, Spain ) %>%
dplyr::mutate(US_cum = cumsum(US),
China_cum = cumsum(China),
Italy_cum = cumsum(Italy),
Germany_cum = cumsum(Germany),
Iran_cum = cumsum(Iran),
Spain_cum = cumsum(Spain),
Korea_cum = cumsum(`Korea, South`))
df_Country_Confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ date,
y = ~ US_cum,
type = "scatter",
mode = "lines+markers",
name = "US") %>%
plotly::add_trace(x = ~ date,
y = ~ China_cum,
type = "scatter",
mode = "lines+markers",
name = "China") %>%
plotly::add_trace(x = ~ date,
y = ~ Italy_cum,
type = "scatter",
mode = "lines+markers",
name = "Italy") %>%
plotly::add_trace(x = ~ date,
y = ~ Iran_cum,
type = "scatter",
mode = "lines+markers",
name = "Iran") %>%
plotly::add_trace(x = ~ date,
y = ~ Germany_cum,
type = "scatter",
mode = "lines+markers",
name = "Germany") %>%
plotly::add_trace(x = ~ date,
y = ~ Korea_cum,
type = "scatter",
mode = "lines+markers",
name = "S.Korea") %>%
plotly::add_trace(x = ~ date,
y = ~ Spain_cum,
type = "scatter",
mode = "lines+markers",
name = "Spain") %>%
plotly::layout(title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Cum. Confirmed"),
xaxis = list(title = ""),
# paper_bgcolor = "black",
plot_bgcolor = "ffffcc",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```
### Cum Deaths by Country
```{r}
df_Country_Death <- df_Country %>%
dplyr:: select(date, Country.Region, death)%>%
tidyr::pivot_wider(names_from = Country.Region,
values_from = death) %>%
dplyr::select(date, US, China, Italy, Iran, Germany, `Korea, South`, Spain) %>%
dplyr::mutate(US_cum = cumsum(US),
China_cum = cumsum(China),
Italy_cum = cumsum(Italy),
Germany_cum = cumsum(Germany),
Iran_cum = cumsum(Iran),
Spain_cum = cumsum(Spain),
Korea_cum = cumsum(`Korea, South`))
df_Country_Death %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ date,
y = ~ US_cum,
type = "scatter",
mode = "lines+markers",
name = "US") %>%
plotly::add_trace(x = ~ date,
y = ~ China_cum,
type = "scatter",
mode = "lines+markers",
name = "China") %>%
plotly::add_trace(x = ~ date,
y = ~ Italy_cum,
type = "scatter",
mode = "lines+markers",
name = "Italy") %>%
plotly::add_trace(x = ~ date,
y = ~ Iran_cum,
type = "scatter",
mode = "lines+markers",
name = "Iran") %>%
plotly::add_trace(x = ~ date,
y = ~ Germany_cum,
type = "scatter",
mode = "lines+markers",
name = "Germany") %>%
plotly::add_trace(x = ~ date,
y = ~ Korea_cum,
type = "scatter",
mode = "lines+markers",
name = "S.Korea") %>%
plotly::add_trace(x = ~ date,
y = ~ Spain_cum,
type = "scatter",
mode = "lines+markers",
name = "Spain") %>%
plotly::layout(title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Cum. Death"),
xaxis = list(title = ""),
# paper_bgcolor = "black",
plot_bgcolor = "ffcccc",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```
row
-----------------------------------------------------------------------
### Cum Recovered by Country
```{r}
df_Country_Recovered <- df_Country %>%
dplyr:: select(date, Country.Region, recovered)%>%
tidyr::pivot_wider(names_from = Country.Region,
values_from = recovered) %>%
dplyr::select(date, US, China, Italy, Iran, Germany, `Korea, South`, Spain ) %>%
dplyr::mutate(US_cum = cumsum(US),
China_cum = cumsum(China),
Italy_cum = cumsum(Italy),
Germany_cum = cumsum(Germany),
Iran_cum = cumsum(Iran),
Spain_cum = cumsum(Spain),
Korea_cum = cumsum(`Korea, South`))
df_Country_Recovered %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ date,
y = ~ US_cum,
type = "scatter",
mode = "lines+markers",
name = "US") %>%
plotly::add_trace(x = ~ date,
y = ~ China_cum,
type = "scatter",
mode = "lines+markers",
name = "China") %>%
plotly::add_trace(x = ~ date,
y = ~ Italy_cum,
type = "scatter",
mode = "lines+markers",
name = "Italy") %>%
plotly::add_trace(x = ~ date,
y = ~ Iran_cum,
type = "scatter",
mode = "lines+markers",
name = "Iran") %>%
plotly::add_trace(x = ~ date,
y = ~ Germany_cum,
type = "scatter",
mode = "lines+markers",
name = "Germany") %>%
plotly::add_trace(x = ~ date,
y = ~ Korea_cum,
type = "scatter",
mode = "lines+markers",
name = "S.Korea") %>%
plotly::add_trace(x = ~ date,
y = ~ Spain_cum,
type = "scatter",
mode = "lines+markers",
name = "Spain") %>%
plotly::layout(title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Cum. Recovered"),
xaxis = list(title = ""),
# paper_bgcolor = "black",
plot_bgcolor = "99ff99",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```
### Cum Active by Country
```{r}
df_Country_Active <- df_Country %>%
dplyr:: select(date, Country.Region, active)%>%
tidyr::pivot_wider(names_from = Country.Region,
values_from = active) %>%
dplyr::select(date, US, China, Italy, Iran, Germany, `Korea, South`, Spain) %>%
dplyr::mutate(US_cum = cumsum(US),
China_cum = cumsum(China),
Italy_cum = cumsum(Italy),
Germany_cum = cumsum(Germany),
Iran_cum = cumsum(Iran),
Spain_cum = cumsum(Spain),
Korea_cum = cumsum(`Korea, South`))
df_Country_Active %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ date,
y = ~ US_cum,
type = "scatter",
mode = "lines+markers",
name = "US") %>%
plotly::add_trace(x = ~ date,
y = ~ China_cum,
type = "scatter",
mode = "lines+markers",
name = "China") %>%
plotly::add_trace(x = ~ date,
y = ~ Italy_cum,
type = "scatter",
mode = "lines+markers",
name = "Italy") %>%
plotly::add_trace(x = ~ date,
y = ~ Iran_cum,
type = "scatter",
mode = "lines+markers",
name = "Iran") %>%
plotly::add_trace(x = ~ date,
y = ~ Germany_cum,
type = "scatter",
mode = "lines+markers",
name = "Germany") %>%
plotly::add_trace(x = ~ date,
y = ~ Korea_cum,
type = "scatter",
mode = "lines+markers",
name = "S Korea") %>%
plotly::add_trace(x = ~ date,
y = ~ Spain_cum,
type = "scatter",
mode = "lines+markers",
name = "Spain") %>%
plotly::layout(title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Cum. Active"),
xaxis = list(title = ""),
# paper_bgcolor = "black",
plot_bgcolor = "99ddff",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```
MAPS
====================================================================================
**Map**
```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
dplyr::filter(cases > 0) %>%
dplyr::group_by(Country.Region,Province.State,Lat,Long,type) %>%
dplyr::summarise(cases = sum(cases)) %>%
dplyr::mutate(log_cases = 2 * log(cases)) %>%
dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red","green"), domain = c("confirmed", "death","recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk( function(df) {
map_object <<- map_object %>%
addCircleMarkers(data=cv_data_for_plot.split[[df]],
lng=~Long, lat=~Lat,
# label=~as.character(cases),
color = ~pal(type),
stroke = FALSE,
fillOpacity = 0.65,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol=c("type","cases","Country.Region","Province.State")),
group = df,
# clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(noHide = F,
direction = 'auto'))
})
map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
```
EVENTS
====================================================================================
```{r}
df_Country_Confirmed <- df_Country %>%
dplyr:: select(date, Country.Region, confirmed)%>%
tidyr::pivot_wider(names_from = Country.Region,
values_from = confirmed) %>%
dplyr::select(date, US, China, Italy, Iran, Germany, `Korea, South`, Spain, India, France, Japan) %>%
dplyr::mutate(US_cum = cumsum(US),
China_cum = cumsum(China),
Italy_cum = cumsum(Italy),
Germany_cum = cumsum(Germany),
Iran_cum = cumsum(Iran),
India_cum = cumsum(India),
Spain_cum = cumsum(Spain),
France_cum = cumsum(France),
Japan_cum = cumsum(Japan),
Korea_cum = cumsum(`Korea, South`))
df_Country_Confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ date,
y = ~ US_cum,
type = "scatter",
mode = "lines+markers",
name = "US") %>%
plotly::add_trace(x = ~ date,
y = ~ China_cum,
type = "scatter",
mode = "lines+markers",
name = "China") %>%
plotly::add_trace(x = ~ date,
y = ~ Italy_cum,
type = "scatter",
mode = "lines+markers",
name = "Italy") %>%
plotly::add_trace(x = ~ date,
y = ~ Iran_cum,
type = "scatter",
mode = "lines+markers",
name = "Iran") %>%
plotly::add_trace(x = ~ date,
y = ~ Germany_cum,
type = "scatter",
mode = "lines+markers",
name = "Germany") %>%
plotly::add_trace(x = ~ date,
y = ~ Korea_cum,
type = "scatter",
mode = "lines+markers",
name = "S.Korea") %>%
plotly::add_trace(x = ~ date,
y = ~ India_cum,
type = "scatter",
mode = "lines+markers",
name = "India") %>%
plotly::add_trace(x = ~ date,
y = ~ France_cum,
type = "scatter",
mode = "lines+markers",
name = "France") %>%
plotly::add_trace(x = ~ date,
y = ~ Spain_cum,
type = "scatter",
mode = "lines+markers",
name = "Spain") %>%
plotly::add_trace(x = ~ date,
y = ~ Japan_cum,
type = "scatter",
mode = "lines+markers",
name = "Japan") %>%
plotly::add_annotations(x = as.Date("2020-02-13"),
y = 44779,
text = paste("CHINA MODIFIES TESTING"),
font = list(color = '#ff9900', family = 'calibri', size = 10),
xref = "x",
yref = "y",
arrowhead = 4,
arrowhead = 3,
arrowsize = 0.4,
showarrow = TRUE,
ax = 50,
ay = -40)%>%
plotly::add_annotations(x = as.Date("2020-01-23"),
y = 643,
text = paste("HUBEI LOCKS DOWN"),
font = list(color = '#ff9900', family = 'calibri', size = 10),
xref = "x",
yref = "y",
arrowhead = 4,
arrowhead = 3,
arrowsize = 0.4,
showarrow = TRUE,
ax = 50,
ay = -40)%>%
plotly::add_annotations(x = as.Date("2020-02-17"),
y = 70513,
text = paste("ACTIVE CASES DROP"),
font = list(color = '#ff9900', family = 'calibri', size = 10),
xref = "x",
yref = "y",
arrowhead = 4,
arrowhead = 3,
arrowsize = 0.4,
showarrow = TRUE,
ax = 10,
ay = 40)%>%
plotly::add_annotations(x = as.Date("2020-02-18"),
y = 31,
text = paste("KOREA SOCIAL DISTANCES"),
font = list(color = 'brown', family = 'calibri', size = 10),
xref = "x",
yref = "y",
arrowhead = 4,
arrowhead = 3,
arrowsize = 0.4,
showarrow = TRUE,
ax = 50,
ay = -40)%>%
plotly::add_annotations(x = as.Date("2020-03-04"),
y = 5621,
text = paste("NEW CASES DROP"),
font = list(color = 'brown', family = 'calibri', size = 10),
xref = "x",
yref = "y",
arrowhead = 4,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = 50,
ay = -40)%>%
plotly::add_annotations(x = as.Date("2020-03-09"),
y = 9172,
text = paste("ITALY-SHUTS DOWN"),
font = list(color = 'green', family = 'calibri', size = 10),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = 0,
ay = -50)%>%
plotly::layout(title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Total Confirmed Cases"),
xaxis = list(title = ""),
# paper_bgcolor = "black",
#plot_bgcolor = "#ccffff",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
))
```